Customer Lifetime Value Prediction

Documentation and step-by-step development by OpenGenus Intern, Samyak Deshpande

Introduction

This repository contains a Jupyter Notebook titled "Customer Lifetime Value Prediction" created at OpenGenus IQ. The notebook explores the prediction of Customer Lifetime Value (LTV) within the context of the retail industry. The primary objective is to provide insights into customer behavior, identify high-value customers, and devise actionable strategies to optimize revenue and profitability.

Table of Contents

• Overview
• Data Description
• Notebook Structure
• Installation
• How to Use
• Dependencies

The "Customer Lifetime Value Prediction" notebook aims to predict the LTV for customers based on historical transaction data. It employs various data analysis, visualization, and machine learning techniques to derive valuable insights and generate LTV predictions.

Data Description

The notebook utilizes a dataset containing customer transaction records, including purchase dates, revenue generated, and customer identifiers. The data is crucial for building the predictive model and gaining a comprehensive understanding of customer behavior.

Notebook Structure

The notebook is structured as follows:

1. Data Loading and Exploration: The dataset is loaded and explored to gain insights into its structure and characteristics.

2. Feature Engineering: The relevant features are extracted and transformed to enhance the predictive power of the model.

3. Data Preprocessing: Data is cleaned, and categorical columns are converted to numerical form for machine learning compatibility.

4. Customer Segmentation: The notebook performs segmentation of customers based on Recency-Frequency-Monetary (RFM) scores.

5. LTV Prediction: The LTV is predicted using machine learning techniques, and clustering is applied to form LTV segments.

6. Model Evaluation: The model's performance is assessed using various evaluation metrics such as precision, recall, and F1-score.

Installation

To run the notebook locally, you need to have Python and Jupyter Notebook installed on your system. You can install the required libraries by running the following command in your terminal:

pip install pandas numpy matplotlib plotly scikit-learn xgboost

How to Use

• Clone this repository to your local machine.
• Open the notebook using Jupyter Notebook or JupyterLab.
• Execute each cell sequentially to run the code and view the results.
• Modify the notebook or dataset as per your requirements to analyze different aspects of customer lifetime value prediction.

Dependencies

The notebook relies on the following Python libraries:

• pandas
• numpy
• matplotlib
• plotly
• scikit-learn
• xgboost Ensure you have these libraries installed before running the notebook.